Use Cases

Hyperautomating Group Airline Bookings

With JIFFY.ai, you save operational cost, reduce errors, and minimize turnaround times, without compromising efficiency or productivity.

Summary

With the Covid-19 pandemic bringing the world to a halt, the airline industry has dealt with turbulent times. However, as these disruptive times settle and air travel restrictions are eased globally, travel agents and airlines will see an uptick in travel bookings.

It typically takes the customer services team at an airline more than three hours to complete a group booking. It also requires them to exit and re-enter the system several times to add and update passenger data as they create and manage groups. The manual tasks these teams perform include validating the authenticity of the data received in Excel, checking for the names available in the system, making name updates, changing passenger information such as passport details, adding infants to the passenger name record, and sending emails to travel agents when requests are completed. Did you know that automation can handle over 10,000 transactions like this per day, accurately and efficiently?

With JIFFY.ai Automate, you can reduce the manual workload in the group order ticketing process. Our app for group bookings comes with cognitive capabilities, machine learning and natural language processing features, along with flexible end-to-end automation capabilities – both rule-based and intelligent. JIFFY.ai Automate is intuitive, and its plug and play features and very short on-boarding time makes it easy to integrate with your existing infrastructure and applications.

With JIFFY.ai, you can:

  • Improve turnaround times by 300%.
  • Increase customer satisfaction significantly.
  • Manage the process with higher accuracy.
  • Reduce the investment required to manage manual teams and processes.
  • Decrease team turnover and ensure happier employees.Reduce

Impact

JIFFY.ai Automate helped a major US airline to automate their group bookings management and update processes, which included a high volume of requests, redundant and repetitive tasks, high turnaround times and high volumes of manual errors.

Key metrics

300,000+

requests per year
processed more
efficiently and accurately

40,000

hours of
manual labor
saved

80-90%

of processes
automated

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